High-resolution, selective and self-optimizing haptic and electrotactile display and methods of use

ABSTRACT

High-resolution, selective, and self-optimizing haptic and electrotactile display and methods of use. In an embodiment of a feedback system referenced herein, the feedback system includes a prosthesis configured to be worn by an individual, including at least one prosthesis sensor configured to detect a state or condition in an environment of the at least one prosthesis sensor, and at least one actuator in communication with the at least one prosthesis sensor and configured to receive data relating to the detected state or condition and to stimulate a nerve of the individual; a neural sensor positioned upon or within the individual, configured to detect a neural response relating to the stimulation of the nerve by the at least one actuator; and a processor in communication with at least one of the at least one prosthesis sensor, at least one of the at least one actuator, and the neural sensor.

PRIORITY

The present application is related to, claims the priority benefit of,and is a U.S. continuation patent application of U.S. patent applicationSer. No. 15/167,527 to Ward et al., filed May 27, 2016, which: (a)issues as U.S. Pat. No. 10,517,791 on Dec. 31, 2019, and (b) is relatedto, and claims the priority benefit of, U.S. Provisional PatentApplication Ser. No. 62/167,141, filed May 27, 2015. The contents ofeach of the foregoing applications and patent are hereby incorporated byreference in their entireties into this disclosure. The presentapplication also incorporates by reference the entirety of U.S.Provisional Patent Application Ser. No. 61/550,584, filed Oct. 24, 2011,the entirety of International Patent Application Serial No.PCT/US2012/061687, filed Oct. 24, 2012, and the entirety of U.S. patentapplication Ser. No. 14/349,511, filed Apr. 3, 2014.

BACKGROUND

Regarding the electrode-tissue interface, selective activation orinhibition of a specific fiber group is an ongoing challenge. Strategiesinclude altering the shape or pattern of the electrical stimulus, usinga multi-channel cuff electrode, and using a more invasive microelectrodearray. Many investigators have demonstrated enhanced control of targetneuron populations using microelectrode arrays that penetrate the nervewith needle-like probes (e.g., the Utah Array) or flatten the nerve ontoan array of planar electrodes (e.g., the Flat Interface Nerve Electrode,or FINE). Since the recording/stimulating sites can be placed nearer thetarget neurons, selectivity and control is improved. The advantage ofthese alternative electrode configurations comes at the expense ofinvasiveness and increased risk of nerve damage. Furthermore, thesuccess of these alternative electrode configurations is largelydictated by a priori knowledge of target fiber location(s) in the nerve,significant experience and perhaps a bit of luck. While an optimalsolution is a noninvasive therapy, a therapy employing some variant ofthe cuff electrode is the next best thing.

While a cuff electrode is generally safer and less invasive thanmicroelectrode arrays that penetrate or flatten the nerve, their currentdesigns are not ideal for selective nerve stimulation or control. Cuffelectrodes make circumferential electrical contact with a nerve trunk orbranch. With this configuration, all axons in the nerve are exposed tothe excitatory and/or inhibitory stimuli, more so at the periphery dueto a closer proximity to the source of the energy. Since all axons areexposed to the stimulus, it logically follows that all neurons may beactivated if a strong enough stimulus is provided (in terms of pulseduration and amplitude).

Some degree of control is provided based on the natural recruitmentorder of axons by size, proximity to the electrode and degree ofmyelination, but knowledge of the stimulus-response profiles of therespective fiber types is required if precision is needed.Stimulus-response profiles are not collected in most electrical nervestimulation (ENS) applications, because they differ across patients,within the same patient over time, and require tedious stimulus-responsemeasurements for each activation level of interest. Furthermore, thecompound nerve action potential (CNAP) response magnitude is a functionof the biology (e.g., neuron type, temperature and ion composition),electrode viability (e.g., increased impedance or thermal noise due toprotein adsorption and glial encapsulation) and environmental influence(e.g., electrical noise and the effects of certain drugs or chemicals).An adaptive, closed-loop control system is needed to personalize thestimulation and resulting effect to each patient.

Regarding the approaches to control or study meurophysiology, JosephBergmans first introduced the principles and utility of measuring singlemotor axon activation thresholds. He showed that many of the inherentphysiological properties of a single motor fiber is embodied in changesto its activation threshold. Generally speaking, an accurate estimate ofthe nodal membrane potential, which is largely a function of thevoltage-gated receptor type, number and distribution, along with factorsthat influence their function, is inferred from changes in the nodalmembrane activation threshold in response to experimental membranepolarization. The techniques developed by Bergmans were difficult tomaster, however, leaving his research at a standstill for several years.S A Raymond later introduced the “Threshold Hunter,” a closed-loopcircuit that clamps the probably of activation to 50% by dynamicallyadjusting the stimulus pulse duration. This tool significantlysimplified the activation threshold measurement process first introducedby Bergmans.

Hugh Bostock and David Burke later introduced the “Threshold Tracker,” asoftware tool that characterizes properties of the nodal membrane at thepoint of stimulation by processing measured changes in the activationthreshold of a population of neurons within a nerve. The ThresholdTracker was designed to measure changes in motor neuron function due to“metabolic and toxic neuropathies”—factors that influence or degrade theintegrity and function of the nodal membrane—but was later deemedsuitable for the study of sensory neuron function. In contrast to theThreshold Hunter introduced by Raymond, the Threshold Tracker fixespulse width and varies pulse amplitude to maintain a set level of nerveactivation, as inferred from the magnitude of the evoked compound nerveor muscle action potential. The membrane properties of a particularneuron type are determined through activation changes brought about byvarious polarizing and hyperpolarizing stimuli paired with a stimuluspulse having a fixed duration and amplitude.

Vagus nerve stimulation (VNS) is a treatment alternative for manyepileptic and depressed patients whose symptoms are not well managedwith pharmaceutical therapy. Approximately 2-weeks after deviceimplantation, a physician programs the pacemaker-like device to deliverintermittent pulses of current to the left cervical vagus nerve. Thehighest efficacy is typically observed after 1 year, but only afterseveral minimally informed stimulus parameter adjustments. The efficacyof these treatments is far from optimal.

Over the course of weeks to months, a physician systematically tunes thestimulus until the patient and physician feel that the therapy isworking with no adverse or intolerable side effects. If a bothersomeside effect is encountered, the intensity of stimulation is decreaseduntil the side effect disappears. These parameters are maintained untilthe next appointment. Major limitations beyond the subjective nature ofthis approach include 1) the risk of adaption or desensitization to thestimulus, which may make the therapy less effective over time (e.g.,stimulus induced depression of neuronal excitability, or SIDNE), 2) thelack of feedback regarding the type and number of neurons that areactivated when the therapy is effective, and 3) the risk of patientdiscomfort.

All ENS therapies use some form of a stimulus parameter-based dosingsystem. This is problematic, as stimulus parameters are poor predictorsof therapeutic efficacy; each patient and nerve responds uniquely to thesame strength of stimulation, and the relationship between stimulationand the degree of nerve activation changes over time. These factorslimit treatment benefit and contribute to poorer efficacy on a shortertimescale. They also help to explain why the therapeutic mechanisms arenot well understood despite decades of investigation. An objective,informed dosing system is required to improve the efficacy of ENStherapies and to further reduce the number and severity of side effects.

BRIEF SUMMARY

The present disclosure includes disclosure of sensory prosthesis devicesand systems and methods of using the same, including, but not limitedto, high-resolution, selective, and self-optimizing haptic andelectrotactile displays and methods of using the same.

In an exemplary embodiment of a feedback system of the presentdisclosure, the feedback system comprises a prosthesis configured to beworn by an individual, the prosthesis comprising at least one prosthesissensor configured to detect a state or condition in an environment ofthe at least one prosthesis sensor, and at least one actuator incommunication with the at least one prosthesis sensor and configured toreceive data relating to the detected state or condition and tostimulate a nerve of the individual; a neural sensor positioned upon orwithin the individual, the neural sensor configured to detect a neuralresponse relating to the stimulation of the nerve by the at least oneactuator; and a processor in communication with at least one of the atleast one prosthesis sensor, at least one of the at least one actuator,and the neural sensor, the processor configured to control operation ofthe at least one actuator based upon the data relating to the detectedstate or condition from the at least one prosthesis sensor and data froma sensation map, the sensation map comprising sensation data relating toan experienced sensation from a brain of the individual in response tothe neural response.

In an exemplary embodiment of a feedback system of the presentdisclosure, the at least one actuator is configured to directlystimulate the nerve of the individual.

In an exemplary embodiment of a feedback system of the presentdisclosure, the at least one actuator is configured to indirectlystimulate the nerve of the individual via skin of the individual.

In an exemplary embodiment of a feedback system of the presentdisclosure, the at least one actuator is configured to vibrate inresponse to the state or condition indicating vibration.

In an exemplary embodiment of a feedback system of the presentdisclosure, the at least one actuator comprises an array of actuators incommunication with the processor via a plurality of signal pathways.

In an exemplary embodiment of a feedback system of the presentdisclosure, the system further comprises the sensation map, and thesensation data of the sensation map further relates to a secondexperienced sensation from the brain of the individual in response to asecond neural response.

In an exemplary embodiment of a feedback system of the presentdisclosure, the system further comprises the sensation map, and thesensation data of the sensation map further relates to a plurality ofadditional experienced sensations from the brain of the individual inresponse to a corresponding plurality of neural responses.

In an exemplary embodiment of a feedback system of the presentdisclosure, the feedback system is accessible using a data processingsystem in communication with the feedback system, the data processingsystem comprising a data processor in communication with the feedbacksystem, a data storage system, and a user interface system, wherein thedata storage system is configured to store data processed by the dataprocessor from the feedback system, and wherein the user interfacesystem is configured to obtain inputs from a user to control operationof the data processor.

In an exemplary embodiment of a feedback system of the presentdisclosure, the data processor is controllable by a second dataprocessing system in communication with the data processor through anetwork.

In an exemplary embodiment of a feedback system of the presentdisclosure, the actuator comprises a plunger positioned relative to anelectromagnetic coil, the plunger having a tip configured to providephysical pressure to the nerve of the individual, the plunger configuredfor displacement from current flowing through the electromagnetic coil;a spring positioned relative to the plunger, the spring configured tooppose a force related to movement of the plunger; and a pressure sensorpositioned relative to the spring, the pressure sensor configured tomeasure pressure provided by the plunger.

In an exemplary embodiment of a feedback system of the presentdisclosure, the actuator further comprises at least one heating elementconfigured to stimulate the nerve of the individual with heat; and atleast one cooling element configured to stimulate the nerve of theindividual via cooling.

In an exemplary embodiment of a feedback system of the presentdisclosure, the plunger provides the physical pressure to the nerve ofthe individual in response to the detected state or condition from theat least one sensor and data from the sensation map relating topressure.

In an exemplary embodiment of a feedback system of the presentdisclosure, the actuator stimulates the nerve of the individual withheat via operation of the at least one heating element in response tothe detected state or condition from the at least one sensor and datafrom the sensation map relating to heat.

In an exemplary embodiment of a feedback system of the presentdisclosure, the actuator stimulates the nerve of the individual withcooling via operation of the at least one cooling element in response tothe detected state or condition from the at least one sensor and datafrom the sensation map relating to cooling.

In an exemplary embodiment of a method of the present disclosure, themethod comprises the steps of collecting sensor data from a sensor of aprosthesis; applying actuation via an actuator of the prosthesiscorresponding to the sensor data to induce a neural response from anerve of an individual wearing the prosthesis; measuring the neuralresponse from the individual; receiving data of a sensationcorresponding to the neural response; generating a sensation maprelating the sensor data to the data of the sensation; and repeating thecollecting, applying, measuring, receiving, and generating steps togenerate a comprehensive sensation map corresponding to the sensor.

In an exemplary embodiment of a method of the present disclosure, thesensor data and the data of the sensation within the comprehensivesensation map is used to further apply actuation of the actuator by wayof movement of a plunger of the actuator to provide physical pressure tothe nerve of the individual in response to the sensor data and the dataof the sensation indicating pressure from the sensor of the prosthesis.

In an exemplary embodiment of a method of the present disclosure, thesensor data and the data of the sensation within the comprehensivesensation map is used to further apply actuation of the actuator by wayof operation of at least one heating element of the actuator to provideheat to the nerve of the individual in response to the sensor data andthe data of the sensation indicating heat from the sensor of theprosthesis.

In an exemplary embodiment of a method of the present disclosure, thesensor data and the data of the sensation within the comprehensivesensation map is used to further apply actuation of the actuator by wayof operation of at least one cooling element of the actuator to providecooling to the nerve of the individual in response to the sensor dataand the data of the sensation indicating cooling from the sensor of theprosthesis.

In an exemplary embodiment of an actuator configured for use with aprosthesis configured to be worn by an individual of the presentdisclosure, the actuator comprises a plunger positioned relative to anelectromagnetic coil, the plunger having a tip configured to providephysical pressure to a nerve of the individual, the plunger configuredfor displacement from current flowing through the electromagnetic coil;a spring positioned relative to the plunger, the spring configured tooppose a force related to movement of the plunger; a pressure sensorpositioned relative to the spring, the pressure sensor configured tomeasure pressure provided by the plunger; at least one heating elementconfigured to stimulate the nerve of the individual with heat; and atleast one cooling element configured to stimulate the nerve of theindividual via cooling; wherein operation of the actuator is controlledusing a processor in communication with the actuator, the processorconfigured to control operation of the actuator based upon data obtainedby a sensor of the prosthesis and a sensation map comprising sensationdata relating to an experienced sensation from a brain of theindividual.

In an exemplary embodiment of an actuator configured for use with aprosthesis configured to be worn by an individual of the presentdisclosure, the actuator is configured to a) provide the physicalpressure based upon pressure data obtained by the sensor of theprosthesis, b) provide the heat based upon heat data obtained by thesensor of the prosthesis, and c) provide the cooling based upon coolingdata obtained by the sensor of the prosthesis.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments and other features, advantages, anddisclosures contained herein, and the matter of attaining them, willbecome apparent and the present disclosure will be better understood byreference to the following description of various exemplary embodimentsof the present disclosure taken in conjunction with the accompanyingdrawings, wherein:

FIG. 1 shows a block diagram of a system, according to an exemplaryembodiment of the present disclosure;

FIG. 2 is a high-level diagram showing the components of adata-processing system, according to an exemplary embodiment of thepresent disclosure;

FIG. 3 shows a top-down view of FIG. 6, according to an exemplaryembodiment of the present disclosure;

FIG. 4 shows exemplary experimental data of the present disclosure. TheCNAP recording location was the left sciatic nerve. The stimuluslocation was the left hind paw (pad). The stimulus type wasconstant-current, cathode-first, alternating monophasic stimulation.Qst/phase increases with Trial Number. Each trace is a mean of N=4 CNAPresponses.

FIGS. 5A-5B show experimental data and predicted data, according toexemplary embodiments of the present disclosure;

FIG. 6 shows experimental data of neuron response to hindpawstimulation, according to an exemplary embodiment of the presentdisclosure;

FIG. 7 shows exemplary experimental data of neuron response to hindpawstimulation of the present dislcosure. The CNAP recording location wasthe left sciatic nerve. The stimulus location was the left hind paw(pad). The stimulus type was constant-current, cathode-first,alternating monophasic stimulation. Qst/phase increases with stimulusnumber. Each trace is a CNAP response to a single stimulus. Stimulusintensity is increased every 4th stimulus.

FIG. 8 shows a graphical representation of an example user interface(UI) for receiving from a user data of intensities of varioussensations, e.g., for classifying sensations.

FIGS. 9A-9E show examples of evoked responses measured with varioussensors, according to exemplary embodiments of the present disclosure;

FIGS. 10A-10B show examples of classifications of evoked responses,according to exemplary embodiments of the present disclosure;

FIGS. 10C-10D show examples of classifications of nerves, according toexemplary embodiments of the present disclosure;

FIG. 11 shows an example UI for classifying sensations used with ahandheld electronic device, according to an exemplary embodiment of thepresent disclosure;

FIGS. 12A-12D show examples of updating an evoked response databaseuseful for a pattern classifier, according to exemplary embodiments ofthe present disclosure;

FIG. 12D shows data from the left cervical vagus nerve of a rat,according to an exemplary embodiment of the present disclosure;

FIGS. 13A-13F show examples of updating a nerve activation profile(NAP), according to exemplary embodiments of the present disclosure;

FIGS. 14A-14E show examples of nerve response to an electrical stimulus,according to exemplary embodiments of the present disclosure;

FIG. 15 graphically represents a graphical UI for autonomous neuralcontrol, according to an exemplary embodiment of the present disclosure;

FIG. 16 shows an example nerve structure indicating nerve stimulus aswell as measurement of nerve response, according to an exemplaryembodiment of the present disclosure;

FIG. 17 shows an elevational cross-section of an actuator, according toan exemplary embodiment of the present disclosure;

FIG. 18 shows a plan view of an actuator array, according to anexemplary embodiment of the present disclosure; and

FIG. 19 shows a pathway of feedback, response data, and experience datarelating to cuff electrodes applied to/within a left hind limb of a rat,according to an exemplary embodiment of the present disclosure.

The attached drawings are for purposes of illustration and are notnecessarily to scale.

An overview of the features, functions and/or configurations of thecomponents depicted in the various figures will now be presented. Itshould be appreciated that not all of the features of the components ofthe figures are necessarily described. Some of these non-discussedfeatures, such as various couplers, etc., as well as discussed featuresare inherent from the figures themselves. Other non-discussed featuresmay be inherent in component geometry and/or configuration.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of this disclosure is thereby intended. The terms “I,”“we,” “our” and the like which may be referenced herein do not refer toany specific individual or group of individuals.

Throughout this description, some aspects are described in terms thatwould ordinarily be implemented as software programs. Those skilled inthe art will readily recognize that the equivalent of such software canalso be constructed in hardware, firmware, or micro-code. Becausedata-manipulation algorithms and systems are well known, the presentdescription is directed in particular to algorithms and systems formingpart of, or cooperating more directly with, systems and methodsdescribed herein. Other aspects of such algorithms and systems, andhardware or software for producing and otherwise processing signals ordata involved therewith, not specifically shown or described herein, areselected from such systems, algorithms, components, and elements knownin the art. Given the systems and methods as described herein, softwarenot specifically shown, suggested, or described herein that is usefulfor implementation of any aspect is conventional and within the ordinaryskill in such arts.

The present disclosure includes disclosure of autonomous neural control(ANC), a nerve activation control system designed to eliminate patientresponse variability and the detrimental effects of the foreign bodyresponse at the device-tissue interface. In rats, ANC rapidly learns howto most efficiently activate any proportion of vagal A, B, and/or Cfibers over time. It provides a new dosing mechanism based on neuralactivation. In real time, ANC systematically decodes evoked compoundnerve action potential (CNAP) responses to construct a patient-specificnerve activation profile (NAP), which describes how each neuronpopulation in the nerve will respond to any strength of stimulation.Over the course of ENS therapy, ANC continuously refines the NAP toimprove its prediction accuracy and adapt to circadian, drug-induced, orimmune mediated changes at the device-tissue interface.

ANC refines the electrical stimulus, within safe limits, to selectivelycontrol nerve activation on a patient-to patient, nerve-to-nerve andneuron-to-neuron basis. By providing consistent nerve activation, ANCallows reproducible experiments to systematically delineate thetherapeutic mechanisms of VNS or other form of ENS therapy. Furthermore,biological markers of treatment response may be measured and classifiedwith respect to the NAP, simplifying the development of fullypersonalized, closed-loop control systems for treating diverseneurological diseases.

For physicians, ANC will 1) establish an objective, standardized dosingsystem based on the level of nerve/neuron activation or inhibition,expressed as a percentage of maximal nerve/neuron activation, 2)eliminate the complicated, time consuming stimulus parameter tuningprocess, 3) provide a simple mechanism to adjust the relative ratios ofA, B and C fiber activation, and 4) ensure that therapeutic nerve/neuronactivation is maintained over time. For patients, ANC can 1) improveefficacy and enhance the overall quality of ENS therapy, 2) reduce thenumber of doctor visits, and 3) help extend device lifetime by reducingenergy waste from excessive stimulation.

Prostheses and systems according to various aspects herein can restoresensation through a lost limb (e.g., an amputated arm) in a way thatfeels natural to the user (e.g., the amputee). Various prior prostheseslack sensory feedback. Various prior prostheses stimulate neurons butlack day-to-day usability; for example, some prior prostheses requiredaily calibration. Various prior schemes use sensors and transceiversfor which placement may vary from day to day, and are negativelyaffected in their performance by variations over time in the electricalsensitivities of the user's nerves.

Various aspects herein infer sensory feedback at a patient's brain bymeasuring nerve impulses. Various aspects measure and classify nerveresponse. Various aspects use machine-learning techniques to provideelectrical stimuli that match user-identifiable sensations. Variousaspects adjust stimulus to the nerves in a closed-loop manner to providea desired nerve response. Various aspects can work with existing sensorsor electrodes, e.g., implantable sensors positioned proximal to nervesor external sensors placed on the skin. Implantable sensors can bepositioned, e.g., upstream of a stimulator, anywhere between the brainand stimulator. As used in this document, “upstream” refers to theafferent direction, i.e., travel towards, or proximity to, the brain;“downstream” refers to the efferent direction, i.e., travel away from,or distance from, the brain.

Various nerves are “mixed nerves.” For example, the medial, radial, andulnar nerves are mixed nerves, i.e., carry motor and sensor informationbidirectionally on the same nerve. Different ones of these nervesenervate different portions of the user's hand. Various aspectsstimulate more than one of these nerves simultaneously on concurrentlyto provide a specific pattern of nerve actuation or sensation. Variousexamples use a single interface for multiple nerves. Various aspects usea single interface for both sensing via, and actuation of, prostheticelements.

FIG. 1 shows a block diagram of an exemplary feedback system 50 of thepresent disclosure. In various embodiments referenced herein, systemcomprises a prosthesis 100, e.g., a robotic arm, including one or moresensors 102 that detect state(s) or condition(s) in the environment.Sensors 102, as referenced herein, can effectively and artificiallyintroduce sensations to neural sensors 110, as referenced herein, viaoperation of actuator(s) 104. Actuators 104, e.g., in the prosthesis 100or elsewhere on the user's body, stimulate nerves 106 according to thedetected state(s) or condition(s), e.g., under control of processor 108,discussed below. Actuators 104 can stimulate the nerves 106, e.g.,directly or via the skin, as discussed below. The term “actuator” doesnot require that any actuator 104 cause or bring about any motion.

Neural sensors 110, e.g., implantable sensors, detect neural response(s)corresponding to the actuation and can provide those to processor 108and/or 286 or another device that operates a control loop. In someexamples, a neural sensor 110 includes a small, particularly-shapedpiece of metal that picks up signals from proximal or adjacent nerves106.

An exemplary prosthesis 100, as referenced herein, may also be referredto herein as an advanced robotic arm, with the robotic arms of thepresent disclosure differing from other robotic arms known in the artfor at least the reason that the prosthesis 100 embodiments of thepresent disclosure are able to sense/detect information from theenvironment and from the limb portion of the wearer of said prosthesis100. As discussed in detail herein, various signals from the brain 112or nerves 106 are utilized as data within a sensation map 114, so toprovide feedback back to prosthesis 100. Prosthesis 100 embodiments ofthe present disclosure can therefore effectively replace lost limbfunction, as referenced herein.

In some examples, readings from neural sensors 110 are used to adjustthe operation of actuators 104 in a closed control loop to provide adesired (e.g., stored or predetermined) neural response. The neuralresponse travels to the user's brain 112, where the user experiences acorresponding sensation. The user can provide sensation data during atraining process, as described below, to permit processor 108 or 286 toprovide neural stimuli corresponding to output(s) of sensor(s) 102. Thetraining process can provide, produce, generate, supplement, and/ormodify sensation map 114 as an output. Neural sensors 110, as referencedherein and as shown in FIGS. 1 and 16, for example, can comprisehigh-density electrodes 292 that are effectively implanted in or ontonerves 106 within the body so to operate as referenced herein. As shownin FIG. 16, for example, electrodes 292 can be positioned upon or withina substrate 293, and connected to one another and/or to other elementsof systems 50, 201 via one or more wires or traces 293. Directions forsensory information and motor information are reflected via the arrowsshown in FIG. 16.

For example, in a right-hand or right-arm prosthesis 100, when sensor102 detects pressure corresponding to a handshake, processor 108 or 286can adjust operate actuators 104 based on sensation map 114 to providethe user with the experience, e.g., sensation(s), of feeling the hand ofthe person shaking the user's hand. In some examples, sensors 102include, e.g., a fingertip pressure sensor 102 with a calibratedresponse from the pressure on the sensor 102 to the electrical output ofthat sensor 102. The processor 108 can then use the sensation map 114 todetermine the neural response corresponding to selected pressures on thesensor 102, and provide corresponding stimulation to the nerves 106 viathe actuators 104. This permits assigning user meaning to specificinputs from sensors 102. Various examples include a sensorized roboticarm (prosthesis 100) with known sensor 102 response curves.

The processor 108 is shown outside prosthesis 100 but can be includedtherein. One processor 100 can correspond to one or more prostheses 100,sensors 102, or actuators 104. Any number of processors 108 can be usedto operate a given prosthesis 100, sensor 102, or actuator 104. Theinputs from any number of sensors 102 can be processed by processor 108to determine the output for a single actuator 104. The input from asingle sensor 102 can be processed by processor 108 to determine theoutput for any number of actuators 104. Processor 108, in variousembodiments, is configured to control operation of actuator 104, asreferenced herein.

In some examples, other biological sensors are used in place of neuralsensor 110. For example, galvanic skin response, heart rate, bloodchemistry, or other biological parameters can be used as proxies fornerve response. As such, in embodiments of systems 50 referenced hereinthat utilize a neural sensor 110, such embodiments can instead utilizeone or more other parameters measurable from the patient in lieu of saidneural sensor(s).

In some examples using cutaneous stimulation, actuators 104 stimulatethe skin surface on patients with nerves 106 re-routed to pectoralmuscles. The nerves 106 can be surgically re-routed to create a map ofthe hand on the pectoral muscles, i.e., a mapping between locations onthe muscle and locations on the hand. These aspects can permit restoringsensation via the skin surface corresponding to the re-routed nerves106. In some examples, the skin surface is stimulated and the nerve 106response measured.

In some examples, the particular type of any given neuron (nerve 106)being measured is determined from the response of that neuron. Forexample, axon diameter is positively correlated with speed of impulsetravel. Moreover, different nerves have different, measurable activationthresholds.

Various aspects determine relationships between stimulus (e.g.,electrical or skin stimulus) and neural response, and between neuralresponse and user sensation. Various aspects combine these determinedrelationships to determine relationships between stimulus and usersensation, e.g., from stimulus to sensation or from sensation tostimulus. Various aspects use a determined mapping from sensation tostimulus to determine the stimulus for a sensation corresponding to acondition or state detected by a sensor 102 on a prosthesis 100, e.g.,heat or pressure. Various aspects then apply the determined stimulus tothe user's nerves 106 (e.g., directly or via a cutaneous or otheractuator 104, and likewise throughout) to provide the user with asimulation of directly experiencing the condition or state via thesenses.

As referenced in FIG. 1, system 50 provides for bidirectional feedbackbetween components of prosthesis 100 and other elements of system 50,such as neural sensor 110, processor 108, and/or sensation map 114 anddata contained therein. Various subjective descriptors can comprise datacontained within sensation map 114, relating to various experiencedsensations from the user's brain 112. The self-training algorithmsreferenced herein are used to close the feedback loop referenced herein,relating inputs from sensors 102 of the prosthesis 110 to electrodeswithin neural sensor 110, for example, so to effectively link nerve 106responses and patterns of nerve activity to sensation.

To determine the mapping from stimulation to neural response, neuralsensors 110 are used to measure neural response under various conditionsof stimulation. This can be done during the training process describedbelow.

To determine the mapping from neural response to user sensation, amachine-learning algorithm or other training process is used. The user'snerves are stimulated with various levels and types of stimulation ispresented with various stimulations. The user provides data on whatsensation the user perceived and how intense the sensation was. Examplesensations and level ranges are described herein and shown, e.g., inFIGS. 8 and 11. For example, levels can range from 0 (no sensation) to 9(the strongest sensation of that type the user can image). Theuser-provided sensation data can be clustered, smoothed, or otherwisepostprocessed to form the sensation map 114. In some examples, trainingis a non-real-time input to a prosthetic system and nerve responses area real-time input to the system.

In some examples, a user goes through a training process for each sensor102. In some examples, the sensation map 114 is stored and can be usedfor a user-selectable, amount of time, e.g., as long as the user iscomfortable with the sensations produced by actuation of sensor 102. Insome examples, the training process includes monitoring sensor 102 andneural sensor 110 outputs while the user performs actions such aseveryday tasks. The user can provide sensation data while performing thetasks, and the sensation map 114 can be adjusted in realtime until theuser indicates that the sensations associated with the task areacceptable (e.g., whether or not “it feels right”). The training processcan be used with any number or combination of sensors 102 and actuators104, e.g., HETDisp actuators discussed below or conventional pressure ortemperature sensors or other actuators.

FIGS. 17 and 18 show examples of an actuator 104 (“HETDisp”), accordingto various aspects, including a moveable element, e.g., a vibratoryelement (a plunger 304, as referenced in further detail herein), that iselectrically conductive. This permits the vibratory element (here, theplunger 304) to be used for skin pressure actuation and electricalstimulation, e.g., of sticking or other surface frictioncharacteristics. The HETDisp also includes heating elements 316 and/orcooling elements 314 that can provide sensations of hot and cold. Anycombination of electrical, vibratory, heating, and cooling actuationscan be provided concurrently or sequentially to produce desired nerveresponses. The heating and cooling elements 316, 314 are examples ofheat sources and heat sinks, respectively. In some examples, athermoelectric element or other device is used that can selectively heator cool (a “heat source/sink”).

In some examples, a plurality of actuators 104 are arranged in a patch(also referred to herein as an array 400) over a portion of the user'sbody, e.g., in a matrix arrangement. The actuators 104 can be controlledusing passive-matrix or active-matrix display strategies commonly usedin softcopy displays. The matrix (array 400) of actuators 104 caninclude any number or combination of HETDisp or prior actuators 104, andcan be a single row or column. Actuators 104 are not constrained to liealong a straight line.

At least FIGS. 3, 4, 6 and 7 show exemplary data of cutaneous hindpawstimulation in rodents. A tuning curve was determined to map stimulationto neural response, and is shown.

Various aspects of the present disclosure provide a scalable,high-resolution and self-optimizing haptic and electro-tactile display(e.g., actuator matrix) that provides sensory feedback to a user throughcustom software-guided, patterned electrical stimulation ofmechanoreceptors and proprioceptors, in isolation or combination,through the skin or an implantable interface, using dense arrays ofelectrodes, in isolation or in combination, with components commonlyused in haptic (vibrating) displays and digital (visual) displays. Thetechnology solves challenges faced by previous attempts at designingrealistic, usable haptic and electro-tactile displays, namely a lack ofday-to-day reproducibility, an inability to reproduce realisticmulti-component sensory stimuli in users of the device(s), and aninability to scale the technology.

Various aspects provide a scalable, high-resolution and self-optimizinghaptic and electro-tactile display that provides sensory feedback to auser through custom software-guided, patterned electrical stimulation ofmechanoreceptors and proprioceptors, in isolation or combination,through the skin or an implantable interface, using dense arrays ofelectrodes, in isolation or in combination with components commonly usedin haptic (vibrating) displays and digital (visual) displays. Thetechnology solves challenges faced by previous attempts at designingrealistic, usable haptic and electro-tactile displays, namely a lack ofday-to-day reproducibility, an inability to reproduce realisticmulti-component sensory stimuli in users of the device(s), and aninability to scale the technology.

Applications of this technology are numerous. A non-comprehensive listis: Sensory restoration, Remote surgery, Sensory substitution, Exposuretherapy, Rehabilitation therapy, Cognitive behavioral therapy, iPhysicaltherapy, Virtual reality, Aviation, Navigation, Stealth communication,Consumer electronics, Marketing, Apparel, Navigation., Virtual reality.Various aspects relate to electro-tactile displays, haptic displays,electrocutaneous stimulation, sensory restoration, or selectivemechanoreceptor recruitment.

Various aspects of training procedures herein can be used with hapticactuators that use grids of vibrating elements to induce a sensation ina user, or with electro-tactile technology including, e.g., high-speedswitching networks and new electrode or stimulation topologies, ormechanoreceptor activation in isolation versus in combination. Variousaspects herein provide improved day-to-day usability (e.g., do notrequire intensive calibration on a day-to-day or hour-to-hour basis,such as accounting for changes in skin impedance with additional supportcircuitry). Various aspects herein provide an interface that improvesits performance on a day-to-day basis (i.e., it does not requirere-calibration on a day-to-day basis). Various algorithms herein workwith external or implantable haptic, electro-tactile, or combinationhaptic/electro-tactile interfaces, e.g., combinationhaptic/electro-tactile devices/displays. Various aspects are scalable interms of cost, the type and intensity of a sensation, and the resolutionof a sensation.

Various aspects predict a sensation that a user might experience using aparticular combination of stimulus parameters using relationshipsbetween a physiological response, such as a nerve signal, the parametersof stimulation, and the subjective input from a user. This was describedabove with reference to the sensation map 114.

Various aspects correlate a subjective experience of a sensation and thehardware/software that evokes the sensation. Various aspects learnpatterns of stimulation/output that induce a very specific sensation ina user, sensations that can only be understood as real by the user,e.g., due to previous experiences of the sensation in a natural manner.

Various aspects are useful in sensory restoration interfaces forpatients with missing limbs.

Steps of various methods described herein can be performed in any orderexcept when otherwise specified, or when data from an earlier step isused in a later step. Exemplary method(s) described herein are notlimited to being carried out by components particularly identified indiscussions of those methods.

In view of the foregoing, various aspects provide neural stimulation. Atechnical effect is to measure physical properties of a user'senvironment and provide the user corresponding sensations, e.g., inplace of the sensations the user would have received from a limb had itbeen present.

FIG. 2 is a high-level diagram showing the components of an exemplarydata-processing system 201 for analyzing data and performing otheranalyses described herein, and related components. The system 201, asshown in FIG. 2, includes a processor 286, a peripheral system 220, auser interface system 230, and a data storage system 240. The peripheralsystem 220, the user interface system 230 and the data storage system240 are communicatively connected to the processor 286 and/or processor108, shown in FIG. 1. Processor 286 can be communicatively connected tonetwork 250 (shown in phantom), e.g., the Internet or a leased line, asdiscussed below. Prosthesis 100, sensor 102, actuator 104, processor108, or neural sensor 110 can each include one or more of processor 286and/or systems 220, 230, and/or 240, and can each connect to one or morenetwork(s) 250. Processor 286, and other processing devices describedherein, can each include one or more microprocessors, microcontrollers,field-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), programmable logic devices (PLDs), programmable logicarrays (PLAs), programmable array logic devices (PALs), or digitalsignal processors (DSPs).

Processor 286 can implement processes of various aspects describedherein, e.g., training processes. Processor 286 and related componentscan, e.g., carry out processes for reading sensors 102, operatingactuators 104, running closed- or open-loop control laws, and/ordetermining sensation maps 114, for example.

Processors 286 can be or include one or more device(s) for automaticallyoperating on data, e.g., a central processing unit (CPU),microcontroller (MCU), desktop computer, laptop computer, mainframecomputer, personal digital assistant, digital camera, cellular phone,smartphone, or any other device for processing data, managing data, orhandling data, whether implemented with electrical, magnetic, optical,biological components, or otherwise.

The phrase “communicatively connected” includes any type of connection,wired or wireless, for communicating data between devices or processors.These devices or processors can be located in physical proximity or not.For example, subsystems such as peripheral system 220, user interfacesystem 230, and data storage system 240 are shown separately from thedata processing system 286 but can be stored completely or partiallywithin the data processing system 286.

The peripheral system 220 can include or be communicatively connectedwith one or more devices configured or otherwise adapted to providedigital content records to the processor 286 or to take action inresponse to processor 186. For example, the peripheral system 220 caninclude digital still cameras, digital video cameras, cellular phones,or other data processors. The processor 286, upon receipt of digitalcontent records from a device in the peripheral system 220, can storesuch digital content records in the data storage system 240. Theperipheral system 220 can be communicatively connected with prosthesis100, sensor 102, actuator 104, processor 108, and/or neural sensor 110,devices shown in FIGS. 17 and 18, or components of any of thoseelements.

The user interface system 230 can convey information in eitherdirection, or in both directions, between a user 238 and the processor286 or other components of system 201. The user interface system 230 caninclude a mouse, a keyboard, another computer (connected, e.g., via anetwork or a null-modem cable), or any device or combination of devicesfrom which data is input to the processor 286. The user interface system230 also can include a display device, a processor-accessible memory, orany device or combination of devices to which data is output by theprocessor 286. The user interface system 230 and the data storage system240 can share a processor-accessible memory. Example user interfaces 232that can be provided by user interface system 230 are illustrated inFIGS. 8, 11, and 15.

In various aspects, processor 286 includes or is connected tocommunication interface 215 that is coupled via network link 216 (shownin phantom) to network 250. For example, communication interface 215 caninclude an integrated services digital network (ISDN) terminal adapteror a modem to communicate data via a telephone line; a network interfaceto communicate data via a local-area network (LAN), e.g., an EthernetLAN, or wide-area network (WAN); or a radio to communicate data via awireless link, e.g., WIFI or GSM. Communication interface 215 sends andreceives electrical, electromagnetic or optical signals that carrydigital or analog data streams representing various types of informationacross network link 216 to network 250. Network link 216 can beconnected to network 250 via a switch, gateway, hub, router, or othernetworking device.

In various aspects, system 201 can communicate, e.g., via network 250,with a data processing system 202, which can include the same types ofcomponents as system 201 but is not required to be identical thereto.Systems 201, 202 are communicatively connected via the network 250. Eachsystem 201, 202 executes computer program instructions to performfunctions described herein.

Processor 286 can send messages and receive data, including programcode, through network 250, network link 216 and communication interface215. For example, a server can store requested code for an applicationprogram (e.g., a JAVA applet) on a tangible non-volatilecomputer-readable storage medium to which it is connected. The servercan retrieve the code from the medium and transmit it through network250 to communication interface 215. The received code can be executed byprocessor 286 as it is received, or stored in data storage system 240for later execution.

Data storage system 240 can include or be communicatively connected withone or more processor-accessible memories configured or otherwiseadapted to store information. The memories can be, e.g., within achassis or as parts of a distributed system. The phrase“processor-accessible memory” is intended to include any data storagedevice to or from which processor 286 can transfer data (usingappropriate components of peripheral system 220), whether volatile ornonvolatile; removable or fixed; electronic, magnetic, optical,chemical, mechanical, or otherwise. Exemplary processor-accessiblememories include but are not limited to: registers, floppy disks, harddisks, tapes, bar codes, Compact Discs, DVDs, read-only memories (ROM),erasable programmable read-only memories (EPROM, EEPROM, or Flash), andrandom-access memories (RAMs). One of the processor-accessible memoriesin the data storage system 240 can be a tangible non-transitorycomputer-readable storage medium, i.e., a non-transitory device orarticle of manufacture that participates in storing instructions thatcan be provided to processor 286 for execution.

In an example, data storage system 240 includes code memory 241, e.g., aRAM, and disk 243, e.g., a tangible computer-readable rotational storagedevice or medium such as a hard drive. Computer program instructions areread into code memory 241 from disk 243. Processor 286 then executes oneor more sequences of the computer program instructions loaded into codememory 241, as a result performing process steps described herein. Inthis way, processor 286 carries out a computer implemented process. Forexample, steps of methods described herein, blocks of the flowchartillustrations or block diagrams herein, and combinations of those, canbe implemented by computer program instructions. Code memory 241 canalso store data, or can store only code.

Various aspects described herein may be embodied as systems or methods.Accordingly, various aspects herein may take the form of an entirelyhardware aspect, an entirely software aspect (including firmware,resident software, micro-code, etc.), or an aspect combining softwareand hardware aspects These aspects can all generally be referred toherein as a “service,” “circuit,” “circuitry,” “module,” or “system.”

Furthermore, various aspects herein may be embodied as computer programproducts including computer readable program code (“program code”)stored on a computer readable medium, e.g., a tangible non-transitorycomputer storage medium or a communication medium. A computer storagemedium can include tangible storage units such as volatile memory,nonvolatile memory, or other persistent or auxiliary computer storagemedia, removable and non-removable computer storage media implemented inany method or technology for storage of information such ascomputer-readable instructions, data structures, program modules, orother data. A computer storage medium can be manufactured as isconventional for such articles, e.g., by pressing a CD-ROM orelectronically writing data into a flash memory. In contrast to computerstorage media, communication media may embody computer-readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transmissionmechanism. As defined herein, computer storage media do not includecommunication media. That is, computer storage media do not includecommunications media consisting solely of a modulated data signal, acarrier wave, or a propagated signal, per se.

The program code includes computer program instructions that can beloaded into processor 286 (and possibly also other processors), andthat, when loaded into processor 286, cause functions, acts, oroperational steps of various aspects herein to be performed by processor286 (or other processor). Computer program code (software) for carryingout operations for various aspects described herein may be written inany combination of one or more programming language(s), and can beloaded from disk 243 into code memory 241 for execution. The programcode may execute, e.g., entirely on processor 286, partly on processor286 and partly on a remote computer connected to network 250, orentirely on the remote computer.

Regarding stimulus artifact suppression, a limited CNAP conductiondistance is available along the left cervical vagus nerve of rodents(e.g., ˜5-15 mm of exposed nerve in a 280-300 g rat). As a result, CNAPresponse peaks often coincide with the stimulus artifact, necessitatingthe use of an artifact suppression method. It is believed that thepresent disclosure includes the initial disclosure of the demonstrationof effective and reliable stimulus artifact suppression usingcathode-first, alternating monophasic stimulation in the peripheralnervous system at conduction distances less than 1 cm.

FIGS. 9A-9E summarizes the method of CNAP extraction and averaging usingthe measured response to a 1-s train of cathode-first, alternatingmonophasic stimulation (I_(st)=stimulus pulse amplitude=0.058 mA;t_(st)=stimulus pulse width=0.4 ms; PRF=pulse repetition frequency=20Hz; t_(train)=stimulus train duration=1 s; Fs=sampling frequency=50 kHz;pass-band=0.001 to 10 kHz). The raw cathodal and anodal stimulusartifacts are shown in black and grey, respectively. To remove any DCoffset, the average of the raw response waveform is subtracted from therecording. Then, ANC segments the raw response waveform into N periodsof responses (N=PRF*t_(train)=20). Each period of the response waveformis then further segmented and grouped into clusters of cathodal andanodal response waveforms, respectively (FIG. 9B). The cathodal andanodal stimulus artifacts are symmetric in the recordings due to thesymmetry of the anodal and cathodal phases of stimulation and thenatural orientation of the recording electrodes along equipotentiallines of the electric field radiating from the stimulating electrodes.The sum of each anodal and cathodal response waveform yields a clusterof artifact-free vagal nerve responses to stimulation (FIG. 9C). Themean CNAP response waveform (FIG. 9E) is the averaged cluster of CNAPresponses in FIG. 1C and the sum of the mean cathodal and anodalresponse waveforms in FIG. 9D. A Shapiro-Wilk test for normality givesno evidence that the artifact-free stimulus responses are not normallydistributed (Prob>z=0.889 when stimulating at 20 Hz for 1 s). Therefore,a response at any point in the signal is significantly different from 0V (at α=0.05) if the mean response and 95% CI do not cross the abscissa.

As noted above, FIGS. 9A-9E include a summary of the cathode-first,alternating-monophasic stimulation method used by ANC. ANC suppressesstimulus artifacts via a symmetric stimulation method in which thecathodal and anodal phases of stimulation have an identical shape andopposite polarity. In most cases, nerve fibers are only activated inresponse to the cathodal pulse (the inset overlays the stimulus waveformin red). ANC clusters each cathodal and anodal response to a train ofbiphasic stimuli (FIG. 9A), clusters the cathodal and anodal responsewaveforms (FIG. 9B), computes the artifact-free responses to each periodof stimulation by summing the cathodal and anodal responses within aperiod of stimulation (FIG. 9C), computes the mean cathodal and anodalresponse waveforms (FIG. 9D), and sums the resulting waveforms to yieldthe mean CNAP response (E). The 95% confidence interval is shown in redin (FIG. 9E).

Regarding nerve response classification, ANC deconstructs thestimulus-evoked CNAP, recorded at a fixed distance from the stimulatingcathode, to estimate the level and type of nerve fiber activation.Conduction velocity is used to identify distinct nerve fiber groups(i.e., neuron populations), referred to as A (fast, myelinated fibers),B (slow, myelinated fibers), or C (slow, unmyelinated fibers). FIGS. 10Aand 10B describe this Letter System using data collected from the leftcervical vagus nerve of a female Long-Evans rat. When recording at afixed, known distance from the stimulating cathode, the CNAP responsewaveform peaks separate in time due to the differing conductionvelocities of A, B and C fibers. The maximal CNAP response, otherwisereferred to as maximal activation, is the CNAP response magnitude atwhich an increase in stimulus intensity does not produce an increase inresponse. By individually deriving stimulus-response relationships forA, B and C fibers, the effect of any stimulus pulse on nerve activity isdirectly measurable.

FIGS. 10A, 10B, and 10C can be further described as follows: Anexemplary CNAP classification system (the table included in FIG. 10C) asbuilt into ANC. FIG. 10A shows a mean CNAP response from the leftcervical vagus nerve of rat. Shaded regions in FIG. 10A correspond to aconduction velocity range in FIG. 10B, which enables nerve fiberclassification according to the Letter System. FIG. 10B shows a meanCNAP response from FIG. 10A plotted as a function of conductionvelocity, in m/s (Conduction Distance=8.0±0.5 mm).

Regarding subjective input classification and sensory experienceestimation, and using multi-contact electrodes as opposed to 1-2 channelcuff electrodes 292 (as referenced in further detail herein),stimulus-response relationships are measurable at the fascicular level,improving functional selectivity through electrode number,electrode/contact area, and spatial location relative to thefascicles/neuron populations of interest. We present an additionalalgorithmic approach to further optimize selectivity, using newlydiscovered properties of the stimulus-response relationship to automateoptimal parameter selection on a subject-by-subject, task-by-task,percept-by-percept basis.

For the purpose of sensory restoration, ANC is modified to acceptsubjective feedback describing the quality and intensity of a perceivedsensation via an iPad interface (FIG. 11) or another handheld deviceinterface. ANC relates this information with particular stimulusparameters, sensor location, sensor input signal, and evoked afferentnerve/neuron response patterns. Using a hybrid electro-tactile hapticdevice (TA 2), we will determine whether this modified form of ANC iscapable of reproducing complex sensory experiences via patternedelectrical stimulation of cutaneous mechanoreceptors (TA 3).

As noted above, FIG. 11 shows an exemplary iPad graphical user interface(GUI) with a modified touch perception task will link the subjectiveexperience of a sensation (i.e., the perceived spatial location,quality, and intensity of an evoked sensation relative to theexpected/anticipated sensation), the measured afferent response to thestimulus that evoked the sensation, and the type of stimulus used toevoked the sensation.

Regarding stimulus-response measurement and classification, ANC measuresa series of stimulus-response relationships to construct an empiricalmodel that describes how each fiber type in any nerve of any patientwill respond to any strength of electrical stimulation. This model,known as a nerve activation profile, describes the sensitivity anddynamic range of each fiber type that can be identified in a CNAP. Itcan be constructed in under a minute. ANC continuously updates the NAPto improve its prediction accuracy over time and adapt to the variety offactors that influence the efficacy of stimulation (e.g., circadianeffects, changes in the electrode-tissue interface over time, or fiberdesensitization to stimulation). New enhancements will let subjectstrain ANC to improve upon the user experience and to remove the need forroutine full calibration cycles.

The sensitivity of each fiber group to ENS is evaluated usingstimulus-response data collected at t_(st)=0.4, 0.2 and 0.1 ms. Ifnecessary, the operator may define all stimulus and recording parameters(default parameters: stimulus type=constant current; stimuluswaveform=cathode-first, alternating monophasic stimulation; PRF=20 Hz;t_(train)=1 s; Fs=50 kHz; pass-band=0.001 to 10 kHz). Starting witht_(st)=0.4 ms, ANC incrementally increases the stimulus amplitude,stimulates the nerve, and records that resulting CNAP response. Betweentrials, the mean CNAP response is computed, the peak fiber responses arelocated and classified, and the data are stored in local memory.Following Trial 1, the response magnitude from the target fiber group isalways compared to that from the previous trial. When stimulus intensityis increased and the target fiber response magnitude no longer increases(i.e., if a fiber group is maximally activated), ANC stores the stimulusparameters and responses from the previous trial. Next, ANC decreasesthe stimulus amplitude according to (1) until parameters that yield apredefined percentage of maximal activation are located (e.g., 25%maximal activation, defined as a target fiber response having amagnitude that is 25% of its maximal response magnitude). The sameprocess is repeated using t_(st)=0.2 and 0.1 ms, respectively. An errortolerance of 5% is initially used to classify all fiber responsemagnitudes to account for the effects of noise.

$\begin{matrix}{Q_{n} = {Q_{n - 1}\left( {1 + \frac{V_{\odot} - V_{{CNAP},{n - 1}}}{k \cdot V_{\odot}}} \right)}} & (1)\end{matrix}$

In (1), Q_(n−1) is the stimulus charge per phase from the most recenttrial (in C/Ph), V_(⊙) is the target fiber response voltage (in V),V_(CNAP,n−1) is the fiber response voltage from the most recent trial,and k is a scaling factor that modulates the magnitude of the stimulusintensity adjustment (e.g., when k is greater than unity, it reduces theintensity of the stimulus charge adjustment; when k is less than unity,it amplifies the intensity of the stimulus charge adjustment). The newstimulus pulse amplitude is calculated by dividing the new stimuluscharge per phase, Q_(n), by the pulse duration used in the precedingtrial (i.e., I_(st,n)=Q_(n)/t_(st,n−1)).

Results indicated a rapid loss of C-fiber activation with constantstimulation. For example, FIGS. 12A-12D shows an example of how a nerveadapts to a constant electrical stimulus in a relatively short time(I_(st)=0.2 mA; t_(st)=0.5 ms; PRF=20 Hz; t_(train)=30 s). Linearregression was performed in STATA 12 to test for a statisticallysignificant order effect, a characteristic feature of SIDNE. Aregression model slope coefficient that is significantly different from0 suggests a relationship/change among the CNAP features of interest(i.e., Aγ⁺, B⁺, or C⁺) and stimulus number (i.e., the sequential numberassigned to each cathodal stimulus pulse within the 30-s train ofstimuli delivered at 20 Hz). Significance tests of the slopecoefficients suggest that, with an increasing number of stimuli, Aγfiber excitability increases (p-value=4.82E⁻⁹) and C fiber excitabilitydecreases (p-value=5.34E⁻⁷⁴). A change in stimulus-driven nerveactivation suggests an analogous change in the evoked sensation relativeto the sensation expected by the subject.

FIGS. 12A-12D can be further described as follows. Subsection A of FIG.12A shows a constant stimulus charge of 100 nC/Ph was applied at 20 Hzfor 30 s. The insets show the pulse duration and amplitude of eachstimulus (I_(st)=0.2 mA; t_(st)=0.5 ms). Subsection B of FIG. 12A showsa measured Aγ, B and C peak response latencies relative to stimulusonset (ms). Subsection C of FIG. 12A shows a measured Aγ, B and C fiberpeak response amplitudes relative to baseline (μV). FIG. 12B shows aclustered stimulus response data from B-C. Amplitude and latency valuesassociated with the second deflection of the diphasic fiber responsesare also plotted in grey. FIG. 12C shows a color map representation ofall stimulus response data shown in A-C (N_(CNAP)=600 CNAP responses).Stimulus onset occurs at the intersection of the Stimulus # and t_(CNAP)axes. Note the significant increase in C peak latency and decrease inresponse voltage as Stimulus # increases. FIG. 12D shows a fiber peakvoltage as a function of stimulus number. Aγ fibers become slightly moreexcitable, as inferred from a general increase in the peak amplitudeover the 600 stimuli. B and C fibers show significantly less activationas stimulus number increases.

Regarding autonomous stimulus-response measurement and classification,FIGS. 13A-13F summarize a set of stimulus-response data that ANCcollected from C fibers in the left cervical vagus nerve of the same ratwhose data is represented in at least FIGS. 12A-12D (PRF=20 Hz;t_(train)=1 s; Conduction Distance=8 mm; N_(CNAP)=66 trials×20 CNAPresponses/trial=1320 CNAP responses). FIGS. 13A-13C show all 1320 Aγ, B,and C fiber CNAP response latencies and voltages (N_(parameter)=20responses per stimulus parameter combination; N_(combo)=66 uniqueparameter combinations). Although Aγ and B fiber stimulus-response datais collected, all stimulus intensity adjustments are based on themagnitude of the mean C fiber response in relation to the maximalresponse voltage. In FIG. 13D, data from FIGS. 13A-13C is clustered byresponse voltage and latency. Local minima from each fiber group arealso plotted to demonstrate that other features of the CNAP response,such as peak-to-peak voltage and area, can be measured and used by ANC.A color map of all 1320 CNAP responses is shown in FIG. 13E (voltage isrepresented as a color according to the scale to the right of thefigure). Finally, the mean CNAP responses are plotted by trial in FIG.13F. Mean peak latencies are computed from the latencies of eachindividual stimulus response shown in FIGS. 13A-13E.

FIGS. 13A-13F can be further described as follows, identifyingautonomously collected stimulus-response data from the left cervicalvagus nerve of a female Long-Evans rat. FIG. 13A shows a stimulus chargeper phase, in nC/Ph, for the entire vagal C-fiber activationprofile-mapping period (ANC constructs a unique activation profile foreach stimulated nerve and neuron type; the activation profile serves asa guide when maintaining or adjusting nerve activation for anexperimental or therapeutic purpose). The insets show the pulse durationand amplitude of each stimulus. FIG. 13B shows measured Aγ, B and C peakresponse latencies relative to stimulus onset (ms). Latency increaseswith decreasing stimulus intensity for Aγ and C fibers, but not for Bfibers. FIG. 13C shows measured Aγ, B and C fiber peak responseamplitudes relative to baseline (μV). FIG. 13D shows clustered stimulusresponse data from B-C. Amplitude and latency values associated with thesecond deflection of the diphasic fiber responses are also plotted ingrey. FIG. 13E shows a color map representation of all stimulus responsedata shown in A-C (N_(CNAP)=66 trials×20 CNAP responses/trial=1320 CNAPresponses). Stimulus onset occurs at the intersection of the Stimulus #and t_(CNAP) axes. FIG. 13F shows a mean CNAP response computed fromdata collected during each trial of stimulation (N_(CNAP, avg)=20 CNAPresponses/trial). Stimulus onset is at the intersection of theV_(CNAP, avg) and t_(CNAP) axes.

Regarding the slope-activation relationship, ANC rapidly identifies theparameter space for each fiber group in a nerve in the form of anactivation profile. An activation profile is autonomously constructedfor each fiber type using measured stimulus-response data and a newlydiscovered mathematical formula that relates threshold current (i.e.,rheobase current) to fiber activation level. A set of activationprofiles for each fiber group in a nerve constitutes a nerve activationprofile.

The key to constructing a NAP is in a newly discovered, predictablerelationship between the rheobase current, I_(Rh), and its correspondingfiber activation level, λ (I_(Rh) is the slope of a charge-duration (CD)line described by the Weiss equation). When all possible CD lines areconstructed from a set of stimulus-response data, each will represent aunique activation level, λ. If the slope of each line (i.e., I_(Rh)) isplotted against its corresponding activation level, λ, an exponentialslope-activation relationship is observed. It is unique to each subject,nerve and neuron type, and allows ANC to adapt to changes at thedevice-tissue interface over the course of an experiment or therapy. Toour knowledge, this is the first discovery and documentation of therelationship.

To derive the slope-activation relationship for vagal Aγ, B and Cfibers, ANC first sorts the stimulus-response data in ascending order bythe evoked response voltage. Each fiber response voltage is thennormalized with respect to the maximal recorded response voltage andconverted to a percentage of maximal activation. The largest observedresponse voltage represents maximal activation. All associated stimulusparameters are stored along with the measured nerve responses (In PhaseI, ANC will be enhanced to accept subjective feedback from the user andsensor input from the sensorized prosthesis. ANC will be furtherenhanced with at least 40 neural recording channels and 40 neuralstimulation channels.).

Next, ANC clusters the evoked fiber responses and associated stimulusparameters by activation level (a 5% error tolerance is used bydefault). Within each cluster, the data is sorted by pulse duration(i.e., t_(st)). If multiple entries have the same pulse duration andevoke the same level of activation, they are replaced with an average ofthe duplicate entries. ANC then searches for clusters with at least 2pulse durations represented. Using these data, ANC computes the best-fitCD lines using least-squares linear regression.

The slope of each computed CD line (i.e., I_(Rh)) is plotted against theassociated percent maximal fiber activation, λ. To model theslope-activation relationships, ANC first computes the natural logarithmof each slope. Then, the best linear fit to the naturallogarithm-transformed data is computed using least-squares linearregression. The resulting equation has the form Î_(Rh)=ar^(λ), whereλ=V_(trial)/V_(max)·100 is the percent maximal activation, a is thepredicted slope of the CD line for 0% maximal activation, and r is aconstant that determines the rate at which the slope of a CD lineincreases as activation level increases. In linear form, theslope-activation equation is ln(Î_(Rh))=λ ln(r)+ln(a). If M=ln(r) andB=ln(a), then Î_(Rh)=e^(M λ+B), where M is the slope, B is they-intercept (i.e., the threshold current for 0% maximal activation), andλ is the percent maximal activation. This can be used in place of I_(Rh)within the Weiss equation. In doing so, an equation that can be used topredict how a target fiber group will respond to any strength of ENS iscreated (2). The Weiss equation is shown above (2) for reference.

$\begin{matrix}{{{{Weiss}\text{:}\frac{1}{t_{st}}{\int\limits_{0}^{t_{st}}{I_{st}dT}}} = {\frac{I_{Rh}\left( {t_{st} + \tau_{SD}} \right)}{t_{st}} = {\overset{¯}{I}}_{st}}}{{{NAP}\text{:}\frac{1}{t_{st}}{\int\limits_{0}^{t_{st}}{I_{st}dT}}} = {\frac{e^{{M\lambda} + B}\left( {t_{st} + {\overset{¯}{\tau}}_{SD}} \right)}{t_{st}} = {\overset{¯}{I}}_{st}}}} & (2)\end{matrix}$

FIGS. 14A-14C show the slope-activation data along with the associatedequations and goodness-of-fit metrics for vagal Aγ, B and C fibers(derived using stimulus response data from at least FIGS. 5A and 5B).The slope-activation data for Aγ fibers is least variable (R²=0.98),followed by C (R²=0.86) and then B fibers (R²=0.36). A consistently poorsignal-to-noise ratio is likely to blame for the poor fit to B fiberslope-activation data. A poor fit to the slope-activation data willtranslate to larger predictive errors once the activation maintenancemode of ANC is initiated. The model will evolve as ANC collects moredata, however. Erroneous or inaccurate values in the slope-activationrelationship are replaced once ANC locates the stimulus parameters thatyield precisely the desired response. In connection with the foregoing,FIG. 5A shows a stimulus-response profile of mixed A and C fiber typesin the left sciatic nerve of Rat TMR6, with data presented incharge-duration line format. FIG. 5B shows a stimulus-response profileof mixed A and C fiber types in the left sciatic nerve of Rat TMR6, withdata presented in strength-duration curve format.

FIGS. 14A-14D can be further described as follows. FIGS. 14A-14C showslope-activation data for Aγ (FIG. 14A), B (FIG. 14B) and C fibers (FIG.14C) for a single animal. For each fiber group, the rheobase current,I_(Rh), is plotted against its corresponding level of maximalactivation, λ. Data shows an exponential increase in rheobase currentfor a linear increase in percent maximal activation. Best-fit curves arecalculated for each fiber type through a least-squares linear regressionof the natural logarithm-transformed slope-activation data. Thecoefficients M and B of the slope-activation equation are placed in thegeneralized form of the Weiss equation, producing a single equation thatpredicts how the target fiber type will response to any strength ofstimulation. The goodness-of-fit is best for Aγ fibers (R²=0.98),followed by C and B fibers (R²=0.86 and 0.35, respectively). A poor fitis most closely associated with a poor signal-to-noise ratio. FIG. 14Dshows activation profiles for Aγ (left), B (middle-left) and C(middle-right) fibers with predicted CD lines (top row) and SD curves(bottom row) for 0 to 100% maximal activation, in 10% increments. Theright column shows the NAP, which predicts how all Aγ, B and C fibers inthe nerve will respond to any strength of stimulation.

Regarding the nerve activation profile, the activation profile for eachfiber type is formed from the slope-activation equation and an estimateof the SD time constant, τ_(SD). The absolute value of the mean of thex-intercept values from the CD lines is used as an estimate of τ_(SD).Given the subject, nerve, and fiber-specific constants M, B, and τ_(SD),(2) predicts the population response of any nerve fiber group to anystrength of constant-current stimulation. This unique attribute isespecially evident when solved for λ in (3).

$\begin{matrix}{\lambda = {\frac{1}{M}\left\lbrack {{\ln \left( \frac{{\overset{¯}{I}}_{st} \cdot t_{st}}{t_{st} + {\overset{¯}{\tau}}_{SD}} \right)} - B} \right\rbrack}} & (3)\end{matrix}$

FIG. 14D graphically depicts the activation profiles that ANCconstructed for vagal Aγ, B and C fibers using (2). The activationprofile can be expressed in CD (top row) or SD form (bottom row). Whenthe activation profile from each fiber group is overlaid, the NAP iscomplete. The NAP in FIG. 14D describes how the left cervical vagusnerve of one particular rat will respond to any strength of electricalstimulation. To aid interpretation, predicted CD lines (top row) and SDcurves (bottom row) are shown within the parameter space for [0, 10, 20,. . . , 100]% maximal activation. Note the nonlinear increase in slopewith a linear increase in percent maximal activation, a propertydescribed by the coefficient M in the slope-activation equation. InPhase I, ANC will be modified with the ability to create independentactivation profiles for each recording channel, fascicle, fiberpopulation, mechanoreceptor, nociceptor, and/or thermoceptor. Subjectivefeedback, from the touch perception task (TPT), will be associated witheach activation profile.

ENS holds the potential to modulate or control the function of almostevery tissue in the body. Control is established by artificiallymodulating the firing activity of existing neural pathways withpatterned electrical impulses from an implantable or external device. Tomaximize control over an application requiring sensory restoration via amulti-channel peripheral nerve interface, the correct strength andpattern of ENS is applied to selectively activate and control one ormore specific neural pathways linked to specific types of cutaneoussensory receptors whose combined activation encodes the foundation ofsensation. Establishing control with conventional stimulation paradigmsis problematic, however, as the degree of neural activation in responseto a given dose of stimulus varies greatly from patient to patient(e.g., due to genetic differences, the tissue/immune response to theimplant, or environmental factors) and changes over time in individualpatients.

ANC is a form of artificial intelligence that adjusts stimulusparameters in real time so that control is maintained over one or moreneural pathways that mediate the target therapeutic effect and the offtarget effects (i.e., side effects). With the closed-loop,biofeedback-driven control provided by ANC, the degree of nerve fiberactivation, ranging from 0 to 100%, is controlled in the same manneracross patients and within the same patient over time. ANC serves as atool to advance our understanding of the relationships between thedegree and pattern of neural activation and therapeutic efficacy.Moreover, it allows for the rapid if not immediate deployment ofstimulus parameters that are optimized for each patient, nerve andneuron type. It is a new alternative to the long, burdensome devicetuning system that is currently in use that can pave the way for a newstandard of care.

In at least one technical approach described herein, the presentdisclosure includes disclosure of a developed scalable, high-resolutionand self-optimizing haptic and electro-tactile display to providenaturalistic sensory feedback to TSR patients through software-guided,patterned electrical, mechanical, and thermal stimulation of sensoryreceptors that are accessible through the re-innervated skin surface.Hybrid transducers, each comprising an electrode, solenoid, heatingelement, and cooling element, will be controlled with ANC softwareaccording to decoded afferent (sensory) response patterns, sensorizedprosthesis output, skin temperature and impedance, contact force, andsubjective feedback from the user describing the sensory and emotionalquality and intensity of an evoked sensation. This novel approach tosensory restoration employs multiple degrees of freedom and learningalgorithms to enable personalized sensory experiences that more closelyresemble patient needs and expectations. Table 1 summarizes the sensoryreceptors targeted with this display. Table 1 shows select properties ofmechanoreceptors, nociceptors, thermoceptors and proprioceptors:

TABLE 1 Select properties of mechanoreceptors, nociceptors,thermoceptors and proprioceptors Mode of Receptor Subtype stimulationPercept Fiber type LTA/HT* RA/SA** Mechanoreceptor Meissner corpuscleDynamic Stroking, Aβ LT RA deformation fluttering, slip Paciniancorpuscle Vibration Vibration, grasp, Aβ LT RA pressure Merkelcell-neurite Indentation Shape, texture, Aβ LT SA complex depth finetactile discrimination Ruffini corpuscle Stretch Stretch, direction, AβLT SA hand position, finger position (proprioception) Free nerve endingsTouch Pleasant touch, C LT SA (C fiber LTM) social interaction G-hairLight touch Skin movement Aβ LT RA D-hair Light touch Skin movement AδLT RA Field Stretch Skin stretch Aα, Aβ LT (proprioception) NociceptorMechano-nociceptor Blunt trauma Skin injury, pain Aδ, C HT SAThermal-mechanical Intense heat Burning pain Aδ HT SA Thermal-mechanicalIntense cold Freezing pain C HT SA Polymodal nociceptor Trauma Skininjury, Aδ, C HT SA burning pain Thermoceptor Cool Cold (25° C.) Cool AδLT SA Cold Intense cold Cold C LT SA (<5° C.) Warm Warm (41° C.) Warm CLT SA Heat Hot (>45° C.) Hot Aδ LT SA Proprioceptor Muscle spindleMuscle length/ Muscle movement Aα LT SA primary velocity Muscle spindleMuscle stretch Muscle stretch Aδ LT SA secondary Golgi tendon organMuscle Muscle Aα LT SA contraction contraction Joint capsule/ Flexion/Joint angle, joint Aβ LT SA kinesthetic extension motion *Lowthreshold/high threshold **Rapidly adapting/slowly adapting

The design of the exemplary tactile transducers 295 (also referred to asactuators 104 or elements containing actuators 104) referenced herein isa novel design of a compact tactile device which integrates mechanical,electrical and thermal stimulations for finger tips aiming atintegration with a kinesthetic feedback system. The transducer array isan mxn array of electromechanical actuators with thermal excitations andsensors.

The scheme of the tactile transducer 295 with a single actuator 104 ispresented in FIG. 17. As referenced herein, a transducer 295 may includean actuator 104 and other elements referenced in FIG. 17, for example,and the terms transducer 295 and actuator 104 may be usedinterchangeably in certain respects. As shown in FIG. 17, an exemplaryactuator 104 of the present disclosure comprises a solenoid ofconducting wire 300 (also referred to herein as an electromagnetic coil300, in some embodiments) that is coupled to an armature 302 and amovable plunger 304. Both the armature 302 and the plunger 304 are madeof soft iron in at least one actuator 104 embodiments. Solenoids 300convert electrical current flowing through the coil into a linearmechanical force that produces the displacement of the plunger, asidentified in the two arrows with the “Magnetic Force” reference in FIG.17. When current flows through the solenoid 300, it creates a magneticattraction force between the armature 302 and the plunger 304 and,thereby, reduces the air gap distance between them. The amount of forcegenerated depends on the amplitude of current, number of turns in thecoil 300 and the geometry and position of the armature 302 and plunger304. The design shown in FIG. 17 is adopted for reduced variability ofthe force with the displacement of the plunger 304. The magnetic forceis always attractive irrespective of the direction of the current. Toobtain a bidirectional displacement of the plunger 304, an elasticspring 306 is employed which opposes the magnetic force, as indicated bythe bidirectional arrow with the “Spring Force” reference in FIG. 17. Athin aluminum brush 308 is used in between the coil 300 and the plunger304 to allow smooth sliding of the plunger 304. One or more miniaturepressure sensors 310 is/are attached to one end of the spring 306 tomeasure the pressure on the plunger 304. As shown in FIG. 17, and in atleast one embodiment, an exemplary actuator 104 can be at or about 15 mmhigh, have a width dimension (including the plunger 304, coil 300, andarmature 302, as shown in FIG. 17) of at or about 8 mm, and have a widthdimension of 10 mm when also including the cooling element(s) 314 andheating element(s) 316, referenced in further detail below.

The input of the transducer 295 is a rectangular pulse train generatedfrom a microcontroller and the output is the motion of the plunger 304,such as by way of the tip 305 of plunger 304 contacting the user's skin,which creates fingertip stimulation, as indicated by the arrow with the“Fingertip Force” reference in FIG. 17. The intensity and the frequencyof the mechanical stimulation depend on the amplitude and the frequencyof the pulse train. The pressure sensor 310, in at least one embodiment,takes a direct current (DC) voltage and outputs a variable DC voltageaccording to the pressure change.

Electrical stimulations are provided to the fingertip through themovable plunger 304. A flexible conductive wire 300, as noted above, isattached with the plunger 304 which carries the electrical stimulations.

On the top hard surface 312 of the transducer 295 (or actuator 104),there will be two Peltier elements; one for cooling (one or more coolingelements 314) and one for heating (one or more heating elements 316).These two elements 314, 316 provide thermal stimulation. A Peltiercooler (an exemplary cooling element 314) or a Peltier heater (anexemplary heating element 316) is a solid-state active heat pump whichtransfers heat from one side of the device (actuator 104/transducer 295)to the other, with consumption of electrical energy. It can be usedeither for heating or for cooling. This device is useful when it isnecessary to transfer heat from one medium to another on a small scale.They are about 1 mm² in surface area in at least one embodiment. Thetemperature difference it creates depends on the amplitude of the inputDC current. With an input current of 0.5 A, for example, a temperaturedifference of 72K is available.

FIG. 18 presents the top view of a 4×4 tactile transducer 295/actuator104 array 400. The signal paths for the independent control of eachtransducer 295 are routed between every row and column via signal paths402. A technique of addressing each transducer 295/actuator 104 can besimilar to the strategy for controlling the pixels in a LCD display viaactive matrix addressing, for example. The signal path 402 also containsvarious excitation inputs and sensor outputs, in various embodiments.Arrays 400 can include four actuators 104 (such as an a 2×2 grid), nineactuators 104 (such as in a 3×3 grid), sixteen actuators 104 (such as ina 4×4 grid, as shown in FIG. 18), or more or fewer actuators 104, as maybe desired. In at least one embodiment, array 400 comprises sixteenactuators 400 in a 4×4 grid, with length and width dimensions of at orapproximately 50 mm.

The technology referenced herein solves challenges faced by previousattempts at designing realistic, usable haptic and electro-tactiledisplays, namely a lack of day-to-day reproducibility, an inability toreproduce realistic multi-component sensory stimuli in users of thedevice(s), and an inability to scale the technology. FIG. 17 shows across-section of an exemplary hybrid electro-tactile haptic displayelement with temperature, pressure, and impedance sensing inputs andthermal, mechanical, and electrical outputs. FIG. 18 shows a top view ofa 16-element array. Specialized software will relate afferent nerveresponse patterns to subjective assessments of a sensation. Along withtemperature, pressure, and impedance information, the software willlearn user preference so that the display output matches userexpectations.

Others in the field are focused primarily on haptic technology, whichuses grids of vibrating elements to induce a sensation in a user. Thoseworking with electro-tactile technology focus primarily on improvedcircuitry (e.g., high-speed switching networks and new electrode orstimulation topologies), improved selectivity (e.g., mechanoreceptoractivation in isolation versus in combination, achievable only throughintensive, time-consuming parameter search processes), and improvedday-to-day usability (i.e., approaches that do not require intensivecalibration on a day-to-day or hour-to-hour basis, such as accountingfor changes in skin impedance with additional support circuitry).

The technology disclosed herein differs from existing solutions andapproaches in several ways:

(A) It provides a truly personalized interface that improves itsperformance on a day-to-day basis (i.e., it does not requirere-calibration on a day-to-day basis). For example, continuous responsemonitoring is provided from an implanted, multi-channel electrode array,coupled with an adaptive controller that adjusts output parameters toevoke a particular pattern of neural responses associated with adiscrete or complex sensations, overcomes the performance limitationsimposed upon existing technology due to changes in skin impedance anddiffering orientations of the electro-tactile or haptic interfacerelative to the original calibration position

(B) The framework/platform supports external or implantable haptic,electro-tactile, or combination haptic/electro-tactile interfaces

(C) The technology is scalable (in terms of cost, the type and intensityof a sensation, and the resolution of a sensation)

(D) The technology has the capacity to predict a sensation that a usermight experience using a particular combination of stimulus parametersusing relationships between a physiological response, such as a nervesignal, the parameters of stimulation, and the subjective input from auser

(E) The technology bridges the gap between a subjective experience of asensation and the hardware/software that evokes the sensation (i.e., ithas the capacity to learn patterns of stimulation/output that induce avery specific sensation in a user, sensations that can only beunderstood as real by the user due to previous experiences of thesensation in a natural manner).

The information referenced in FIG. 19 and presented above involves areceptor identification/classification approach, and sensory restorationapproach. Regarding a rodent experiment (the image shown in FIG. 19 isthat of the left hind limb of a rat), it was noted that subjective inputcan only come from human tests, and that subjective feedback canaccelerate the development of the sensory restoration interface.

In the present study, and as shown in FIG. 19, multiple cuff electrodes292 (of exemplary neural sensors 110) were used record afferent fiberactivation, noting that cutaneous electrodes can be used with humanusers. Sural, tibial and common peroneal cuff electrodes 292 wereidentified to aid mechanoreceptor classification by fiber group &physical (spatial) location of the mechanoreceptor. The sciatic cuffenabled conduction and velocity-dependent identification of fiber groupsrepresented in a train of CAP responses arising from mechanoreceptoractivation. The combination of cuffs enabled the classification of a“burst” of responses from a specific fiber population vs. a single CNAPfrom multiple fiber groups. The combination of cuffs also enabled theclassification of a “burst” of responses from a >1 fiber populationlinked to a specific mechanoreceptor subtype.

In human or rat experiments, multiple forms of mechanical stimulationcan be presented to the contralateral hand or hind limb to mimic a “realworld” sensation. In rats, nerve responses are recorded and classifiedwith respect to the mechanical stimulus (e.g., type, texture, gradient,etc.). In humans, nerve responses are recorded and classified withrespect to the mechanical stimulus and the subjective experience of thestimulus, allowing for the design of a fully-personalized sensoryrestoration interface as generally referenced herein, such as aninterface that trains itself by associating context-dependent feedbackfrom the user with specific, fiber-selective patterns of activation asdescribed herein and generally shown in FIG. 1.

Methods of mechanoreceptor response identification from the measuredafferent CNAP response (or response train) provide for fast versus slowadaptation (e.g., duration or type of neural response as a function ofstimulus duration—mechanical or electrical), frequency-selectiveactivation, pressure-selective activation (e.g., intensity ofstimulation, which may activate superficial or deep mechanoreceptors),and spatial-selective activation (e.g., point of stimulation).

Methods of mechanoreceptor-selective stimulation to mimic a “real world”mechanical stimulus (used individually or in combination) can include aninitial ultra high-frequency burst of stimuli to silence rapidlyadapting mechanoreceptors followed by personalized pattern of stimulimatched to the response pattern which a pattern classifier identifiedand stored along with a receptor activation profile (RAP) for slowlyadapting mechanoreceptors in ANC, such as selective stimulation forRuffini corpuscles and Merkel disks with frequency-selective activation,including, but not limited to, Merkel disks (5-15 Hz), Meisnercorpuscles (10-50 Hz), and Pacinian corpuscles (200-300 Hz).

Depth-selective stimulation is also provided, such as burst modulatedstimuli versus conventional rectangular stimuli. This can be implementedfor use to exclude or include superficial or deep mechanoreceptorsubtypes, for example.

Spatial-selective stimulation, such as to choose an optimal electrodepair within a grid of electrodes to limit the spatial extent of thestimulus and to provide a higher-resolution representation of a realworld sensation to the user, can also be implemented. For example, ahigh resolution sensation can be artificially sensed by a user using thecombination of, a high-resolution grid of electrodes with definedspatial locations on the receptor field, parallel stimulation frommultiple electrode pairs chosen according to the sensor locations on aprosthesis, and/or complex, patterned stimuli from each electrode pairfor selective activation of individual or multiple mechanoreceptorsubtypes.

Charge-duration line summary equations for an exemplary TMR6 leftsciatic nerve stimulus response profile were also obtained via operationof an exemplary device 100 of the present application. For example, andfor a mixed A-fiber population (V_(Afibers,Max)=1.473155E-04 V):

-   Maximal (i.e., 100%) activation threshold equation (from measured    data):

Q _(max)=0.00035398*t _(st)+8.4072E-09, [R ²=1], [τ_(SD)=2.375E-05 sec]

-   25% maximal activation threshold equation (from measured data):

Q _(25%Max)=0.00017411*t _(st)+1.1401E-08, [R ²=0.98023],[τ_(SD)=6.5484E-05 sec]

-   Minimal (i.e., 0%) activation threshold equation (predicted from ANC    Model Equations):

Q _(0%Max,Pred)=0.00011415*t _(st)+9.0627E-09, [τ_(SD,Pred)=7.9395E-05sec]

-   Rheobase-%Maximal Activation Relationship

I _(Rh,Pred)=2.3984E-06*%Act+1.1415E-04

For an exemplary C-fiber Population (V_(Cfibers,Max)=4.245585E-04 V):

-   Maximal (i.e., 100%) activation threshold equation (from measured    data):

Q _(Max)=0.00019456*t _(st)+2.1676E-08, [R ²=0.98917],[τ_(SD)=0.00011141 sec]

-   25% maximal activation threshold equation (from measured data):

Q _(25%Max)=0.0001808*t _(st)+1.0393E-08, [R ²=0.99995],[τ_(SD)=5.7482E-05 sec]

-   Minimal (i.e., 0%) activation threshold equation (predicted from ANC    Model Equations):

Q _(0%Max,Pred)=0.00017621*t _(st)+6.9614E-09, [τ_(SD,Pred)=3.9506E-05sec]

-   Rheobase-%Maximal Activation Relationship

I _(Rh,Pred)=1.8350E-07*%Act+1.7621E-04

-   -   For each of the foregoing:    -   1. All numbers are presented in SI units    -   2. %maximal activation can be estimated as a proportion of        V_(Max) for the fiber type of interest    -   3. Q_(Max) and Q_(0%Max,Pred) correspond to the lines plotted in        the accompanying figure    -   4. Q=charge/phase (in coulombs)    -   5. I_(Rh)=rheobase current and slope of charge-duration line (in        amps)    -   6. t_(st)=pulse duration (in sec)    -   7. τ_(SD)=strength-duration time constant and x-intercept (in        sec)

While various embodiments of devices for sensory prosthesis devices andsystems and methods for using the same have been described inconsiderable detail herein, the embodiments are merely offered asnon-limiting examples of the disclosure described herein. It willtherefore be understood that various changes and modifications may bemade, and equivalents may be substituted for elements thereof, withoutdeparting from the scope of the present disclosure. The presentdisclosure is not intended to be exhaustive or limiting with respect tothe content thereof.

The disclosure referenced herein is inclusive of combinations of theaspects described herein. References to “a particular aspect” (or“embodiment” or “version”) and the like refer to features that arepresent in at least one aspect of the present disclosure. Separatereferences to “an aspect” (or “embodiment”) or “particular aspects” orthe like do not necessarily refer to the same aspect or aspects;however, such aspects are not mutually exclusive, unless so indicated oras are readily apparent to one of skill in the art. The use of singularor plural in referring to “method” or “methods” and the like is notlimiting. The word “or” is used in this disclosure in a non-exclusivesense, unless otherwise explicitly noted.

Further, in describing representative embodiments, the presentdisclosure may have presented a method and/or a process as a particularsequence of steps. However, to the extent that the method or processdoes not rely on the particular order of steps set forth therein, themethod or process should not be limited to the particular sequence ofsteps described, as other sequences of steps may be possible. Therefore,the particular order of the steps disclosed herein should not beconstrued as limitations of the present disclosure. In addition,disclosure directed to a method and/or process should not be limited tothe performance of their steps in the order written. Such sequences maybe varied and still remain within the scope of the present disclosure.

1. A feedback system, comprising: a prosthesis configured to be worn byan individual, the prosthesis comprising: at least one prosthesis sensorconfigured to detect a state or condition in an environment of the atleast one prosthesis sensor, and at least one actuator in communicationwith the at least one prosthesis sensor and configured to receive datarelating to the detected state or condition and to stimulate a nerve ofthe individual; a neural sensor positioned upon or within theindividual, the neural sensor configured to detect a neural responserelating to a stimulation of the nerve by the at least one actuator; anda processor in communication with at least one of the at least oneprosthesis sensor, at least one of the at least one actuator, and theneural sensor, the processor configured to control operation of the atleast one actuator based upon the data relating to the detected state orcondition from the at least one prosthesis sensor and data from asensation map.
 2. The feedback system of claim 1, wherein the processoris further configured to predict one or more sensations that theindividual may experience using sensation data from the sensation maprelating to one or more experienced sensations from a brain of theindividual in response to the neural response.
 3. The feedback system ofclaim 1, wherein the at least one actuator is configured to directly orindirectly stimulate the nerve of the individual.
 4. The feedback systemof claim 1, wherein the at least one actuator is configured to vibratein response to the state or condition when the state or conditionindicates vibration.
 5. The feedback system of claim 1, wherein the atleast one actuator comprises an array of actuators in communication withthe processor via a plurality of signal pathways.
 6. The feedback systemof claim 1, wherein the system further comprises the sensation map, andwherein the sensation data of the sensation map further relates to asecond experienced sensation from the brain of the individual inresponse to a second neural response.
 7. The feedback system of claim 1,wherein the system further comprises the sensation map comprisingsensation data that relates to one or more experienced sensations from abrain of the individual in response to one or more neural responses. 8.The feedback system of claim 1, accessible using a data processingsystem in communication with the feedback system, the data processingsystem comprising: a data processor in communication with the feedbacksystem, a data storage system, and a user interface system, wherein thedata storage system is configured to store data processed by the dataprocessor from the feedback system, and wherein the user interfacesystem is configured to obtain inputs from a user to control operationof the data processor.
 9. The feedback system of claim 8, wherein thedata processor is controllable by a second data processing system incommunication with the data processor through a network.
 10. Thefeedback system of claim 1, wherein the actuator comprises: a plungerpositioned relative to an electromagnetic coil, the plunger having a tipconfigured to provide physical pressure to the nerve of the individual,the plunger configured for displacement from current flowing through theelectromagnetic coil; a spring positioned relative to the plunger, thespring configured to oppose a force related to movement of the plunger;and a pressure sensor positioned relative to the spring, the pressuresensor configured to measure pressure provided by the plunger.
 11. Thefeedback system of claim 10, wherein the actuator further comprises atleast one heating element configured to stimulate the nerve of theindividual with heat, at least one cooling element configured tostimulate the nerve of the individual via cooling, or both.
 12. Thefeedback system of claim 10, wherein the plunger provides the physicalpressure to the nerve of the individual in response to the detectedstate or condition from the at least one sensor and data from thesensation map relating to pressure.
 13. The feedback system of claim 11,wherein the actuator stimulates the nerve of the individual with heatvia operation of the at least one heating element in response to thedetected state or condition from the at least one sensor and data fromthe sensation map relating to heat.
 14. The feedback system of claim 11,wherein the actuator stimulates the nerve of the individual with coolingvia operation of the at least one cooling element in response to thedetected state or condition from the at least one sensor and data fromthe sensation map relating to cooling.
 15. A method, comprising thesteps of: collecting sensor data from a sensor of a prosthesis; applyingactuation via an actuator of the prosthesis corresponding to the sensordata to induce a neural response from a nerve of an individual wearingthe prosthesis; receiving data of a sensation corresponding to a neuralresponse from the individual; and generating a sensation map relatingthe sensor data to the data of the sensation.
 16. The method of claim15, further comprising the step of: repeating the collecting, applying,receiving, and generating steps to generate a comprehensive sensationmap corresponding to the sensor, wherein the sensor data and the data ofthe sensation within the comprehensive sensation map is used to furtherapply actuation of the actuator by way of movement of a plunger of theactuator to provide physical pressure to the nerve of the individual.17. The method of claim 15, wherein the sensor data and the data of thesensation within the sensation map is used to further apply actuation ofthe actuator by way of operation of at least one heating element of theactuator to the individual in response to the data of the sensationindicating heat from the sensor of the prosthesis.
 18. The method ofclaim 15, wherein the sensor data and the data of the sensation withinthe sensation map is used to further apply actuation of the actuator byway of operation of at least one cooling element of the actuator toprovide cooling to the individual in response to the data of thesensation indicating cooling from the sensor of the prosthesis.
 19. Anactuator comprising: a plunger positioned relative to an electromagneticcoil, the plunger having a tip configured to provide physical pressureto a nerve of an individual, the plunger configured for displacementfrom current flowing through the electromagnetic coil; at least oneheating element configured to stimulate the nerve of the individual withheat; and at least one cooling element configured to stimulate the nerveof the individual via cooling; wherein operation of the actuator iscontrolled using a processor in communication with the actuator, theprocessor configured to control operation of the actuator based upondata obtained by a sensor of a prosthesis and a sensation map comprisingsensation data relating to an experienced sensation from a brain of theindividual.
 20. The actuator of claim 19, wherein the actuator isconfigured to a) provide physical pressure based upon pressure dataobtained by the sensor of the prosthesis, b) provide the heat based uponheat data obtained by the sensor of the prosthesis, and c) provide thecooling based upon cooling data obtained by the sensor of theprosthesis.